Interference cancellation with improved estimation and tracking for wireless communication

ABSTRACT

Techniques for performing interference cancellation in a wireless (e.g., CDMA) communication system are described. In one aspect, per-bin power estimates for multiple orthogonal bins are derived by estimating at least two components of these power estimates. The components may include, e.g., channel gain, noise and interference, and bin gain. Interference cancellation is performed based on the per-bin power estimates. In another aspect, interference cancellation is performed in multiple stages with fast tracking. A total power estimate and per-bin power estimates are derived for a first stage. A total power estimate is derived for a second stage. Per-bin power estimates are also derived for the second stage based on the total power estimates for the first and second stages and the per-bin power estimates for the first stage. Interference cancellation is performed for each stage based on the per-bin power estimates for that stage.

The present application claims priority to provisional U.S. ApplicationSer. No. 60/748,062, entitled “Accelerated Tracking for Cascaded QLIC,”filed Dec. 6, 2005, assigned to the assignee hereof and incorporatedherein by reference.

BACKGROUND

I. Field

The present disclosure relates generally to communication, and morespecifically to techniques for performing interference cancellation in awireless communication system.

II. Background

A wireless multiple-access communication system can concurrentlycommunicate with multiple wireless devices, e.g., cellular phones.Examples of such multiple-access systems include Code Division MultipleAccess (CDMA) systems, Time Division Multiple Access (TDMA) systems, andFrequency Division Multiple Access (FDMA) systems.

A wireless multiple-access system typically includes many base stationsthat provide communication coverage for a large geographic area. Eachbase station may transmit data to one or more wireless devices locatedwithin its coverage area at any given moment. A given wireless devicemay receive a desired transmission from a serving base station as wellas interfering transmissions from nearby base stations. Theseinterfering transmissions are intended for other wireless deviceslocated within the coverage areas of these nearby base stations but actas interference to this given wireless device. The interference hindersthe wireless device's ability to demodulate the desired transmission andhas a large impact on performance.

There is therefore a need in the art for techniques to demodulate adesired transmission in the presence of interfering transmissions in awireless communication system.

SUMMARY

Techniques for performing interference cancellation in a wirelesscommunication system (e.g., a CDMA system) are described herein. As usedherein, “cancellation” and “suppression” are synonymous terms and areused interchangeably. The techniques perform interference cancellationbased on per-bin power estimates for multiple orthogonal bins, e.g.,Walsh bins.

In an aspect, the per-bin power estimates are derived by estimating atleast two components of these power estimates. The components mayinclude, e.g., channel gain, noise and interference, and bin gain. In anembodiment, initial power estimates {circumflex over (λ)}_(l,n), arederived for the orthogonal bins, e.g., based on received symbols forthese bins. A noise and interference estimate {circumflex over (σ)}_(l)² may then be derived based on (1) an initial power estimate for a nullbin with no transmission or (2) the smallest initial power estimate forall bins. A bin gain estimate ĝ_(l,n) may be derived for each orthogonalbin based on the initial power estimate {circumflex over (λ)}_(l,n) forthat bin and a pilot power estimate. A channel gain estimate ĥ_(l) maybe derived based on a received pilot. The various estimates may bederived with filters having time constants selected to provide goodestimation performance. The power estimate {circumflex over ({circumflexover (λ)}_(l,n) for each orthogonal bin may then be derived based on thechannel gain estimate ĥ_(l), the noise and interference estimate{circumflex over (σ)}_(l) ², and the bin gain estimate ĝ_(l,n) for thatbin. Interference cancellation is performed using the power estimatesfor the orthogonal bins, as described below.

In another aspect, interference cancellation is performed in multiplestages with fast tracking. A total power estimate Ŝ _(l,1) and per-binpower estimates {circumflex over (Λ)} _(l,1) for multiple orthogonalbins are derived for a first stage, e.g., based on the received symbolsfor this stage. A fast filter may be used for the total power estimate,and a slower filter may be used for the per-bin power estimates.Interference cancellation is performed for the first stage based on theper-bin power estimates {circumflex over (Λ)} _(l,1) for this stage. Atotal power estimate Ŝ_(l,2) is derived for a second stage, e.g., basedon the received symbols for this stage. Per-bin power estimates{circumflex over (Λ)} _(l,2) are derived for the second stage based onthe total power estimates Ŝ_(l,1) and Ŝ_(l,2) for the first and secondstages, respectively, and the per-bin power estimates {circumflex over(Λ)} _(l,1) for the first stage. Interference cancellation is performedfor the second stage based on the per-bin power estimates {circumflexover (Λ)} _(l,2) for this stage.

Various aspects and embodiments of the invention are described infurther detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a CDMA system with multiple base stations.

FIG. 2 shows a block diagram of a base station and a wireless device.

FIG. 3 shows a CDMA modulator at the base station.

FIG. 4 shows a single-sector interference canceller.

FIG. 5 shows a process for performing interference cancellation.

FIG. 6A shows another process for performing interference cancellation.

FIG. 6B shows a process for deriving power estimates.

FIG. 7 shows a parallel multi-sector interference canceller.

FIG. 8A shows a cascaded two-sector interference canceller.

FIG. 8B shows a cascaded multi-sector interference canceller.

FIG. 9 shows a parallel two-stage interference canceller.

FIGS. 10A and 10B show two embodiments of a quasi-linear interferencecancellation (QLIC) block.

FIG. 11 shows a cascaded interference canceller with acceleratedtracking.

FIG. 12A shows a QLIC block for the first stage with acceleratedtracking.

FIG. 12B shows a QLIC block for a subsequent stage.

FIG. 13 shows a process for performing cascaded interferencecancellation.

DETAILED DESCRIPTION

The interference cancellation techniques described herein may be usedfor various communication systems such as CDMA, TDMA, FDMA, OrthogonalFDMA (OFDMA), and Single-Carrier FDMA (SC-FDMA) systems. A CDMA systemmay implement one or more CDMA Radio Access Technologies (RATs) such ascdma2000, Wideband-CDNM (W-CDNM), and so on. cdma2000covers IS-2000,IS-856, and IS-95 standards. A TDMA system may implement a RAT such asGSM. These various RATs and standards are known in the art. W-CDMA andGSM are described in documents from a consortium named “3rd GenerationPartnership Project” (3GPP). cdma2000is described in documents from aconsortium named “3rd Generation Partnership Project 2” (3GPP2). 3GPPand 3GPP2 documents are publicly available. An OFDMA system utilizesOFDM to transmit symbols in the frequency domain on orthogonalsubcarriers. An SC-FDMA system transmits symbols in the time domain onorthogonal subcarriers. For clarity, the techniques are described belowfor a CDMA system, which may be a cdma2000 system or a W-CDMA system.

FIG. 1 shows a CDMA system 100 with multiple base stations. Forsimplicity, FIG. 1 shows only three base stations 110 a, 110 b and 110 cand one wireless device 120. A base station is generally a fixed stationthat communicates with the wireless devices and may also be called aNode B (3GPP terminology), an access point, and so on. Each base station110 provides communication coverage for a particular geographic area.The term “cell” can refer to a base station and/or its coverage areadepending on the context in which the term is used. To improve systemcapacity, the base station coverage area may be partitioned intomultiple (e.g., three) smaller areas. Each smaller area is served by arespective base transceiver subsystem (BTS). The term “sector” can referto a BTS and/or its coverage area depending on the context in which theterm is used. For a sectorized cell, the BTSs for all sectors of thatcell are typically co-located within the base station for the cell. Thefollowing description assumes that each cell is partitioned intomultiple sectors. For simplicity, the term “base station” genericallyrefers to a fixed station for a cell as well as a fixed station for asector. A serving base station/sector is a base station/sector withwhich a wireless device communicates.

A wireless device may be fixed or mobile and may also be called a userequipment (UE) (3GPP terminology), a mobile station (cdma2000terminology), a user terminal, and so on. A wireless device may be acellular phone, a personal digital assistant (PDA), a wireless modemcard, and so on. A wireless device may communicate with zero, one, ormultiple base stations on the forward and reverse links at any givenmoment. The forward link (or downlink) refers to the communication linkfrom the base stations to the wireless devices, and the reverse link (oruplink) refers to the communication link from the wireless devices tothe base stations. For simplicity, FIG. 1 shows only transmissions onthe forward link. Wireless device 120 receives a desired transmissionfrom serving base station 110 a via line-of-sight and reflected pathsand also receives interfering transmissions from neighbor base stations110 b and 110 c via line-of-sight and reflected paths.

FIG. 2 shows a block diagram of a base station 110 i and wireless device120. Base station 110 i may be any one of the base stations shown inFIG. 1. For simplicity, FIG. 2 shows base station 110 i having onetransmit antenna and wireless device 120 having one receive antenna. Ingeneral, base station 110 i and wireless device 120 may each be equippedwith any number of antennas. For simplicity, FIG. 2 shows only theprocessing units for data transmission on the forward link.

At base station 110 i, a transmit (TX) data processor 210 receivestraffic data for the wireless devices being served, processes (e.g.,encodes, interleaves, and symbol maps) the traffic data to generate datasymbols, and provides the data symbols to a CDMA modulator 220. As usedherein, a data symbol is a modulation symbol for data, a pilot symbol isa modulation symbol for pilot, a modulation symbol is a complex valuefor a point in a signal constellation (e.g., for M-PSK or M-QAM), asymbol is generally a complex value, and pilot is data that is known apriori by both the base stations and the wireless devices. CDMAmodulator 220 processes the data symbols and pilot symbols as describedbelow and provides a stream of output chips to a transmitter (TMTR) 230.Transmitter 230 processes (e.g., converts to analog, amplifies, filters,and frequency upconverts) the output chip stream and generates a forwardlink signal, which is transmitted from an antenna 232.

At wireless device 120, an antenna 252 receives the forward link signalstransmitted by base station 110 i as well as other base stations.Antenna 252 provides a received signal to a receiver (RCVR) 254.Receiver 254 processes (e.g., filters, amplifies, frequencydownconverts, and digitizes) the received signal and provides receivedsamples to an interference canceller 260. Interference canceller 260suppresses the interference from interfering base stations as describedbelow and provides interference-canceled samples for the serving basestation to a rake receiver 270. Antenna 252 may receive the forward linksignal from the serving base station via one or more signal paths asshown in FIG. 1, and the received signal may include one or more signalinstances (or multipaths) for the serving base station. Rake receiver270 processes all multipaths of interest and provides data symbolestimates, which are estimates of the data symbols sent by the servingbase station. Rake receiver 270 may also be replaced with an equalizeror some other types of receiver. A receive (RX) data processor 280processes (e.g., symbol demaps, deinterleaves, and decodes) the datasymbol estimates and provides decoded data. In general, the processingby rake receiver 270 and RX data processor 280 is complementary to theprocessing by CDMA modulator 220 and TX data processor 210,respectively, at base station 110 i.

Controllers/processors 240 and 290 direct operation at base station 110i and wireless device 120, respectively. Memories 242 and 292 store dataand program codes for base station 110 i and wireless device 120,respectively.

For CDMA, multiple orthogonal channels may be obtained with differentorthogonal codes. For example, multiple orthogonal traffic channels areobtained with different Walsh codes in cdma2000, and multiple orthogonalphysical channels are obtained with different orthogonal variablespreading factor (OVSF) codes in W-CDMA. The orthogonal channels may beused to send different types of data (e.g., traffic data, broadcastdata, control data, pilot, and so on) and/or traffic data for differentwireless devices. The orthogonal channels are appropriately scaled,combined, and spectrally spread across the entire system bandwidth. Thespectral spreading is performed with a spreading code, which is apseudo-random number (PN) sequence in cdma2000 and a scrambling code inW-CDMA. In cdma2000, the channelization with Walsh codes is called“covering”, and the spectral spreading is called “spreading”. In W-CDMA,the channelization with OVSF codes is called “spreading”, and thespectral spreading is called “scrambling”. For clarity, cdma2000terminology (e.g., traffic channel, covering, spreading, and so on) isused in the following description.

FIG. 3 shows a block diagram of CDMA modulator 220 within base station110 i. For simplicity, the following description assumes that N trafficchannels are available for each sector, and each traffic channel isassigned a different Walsh code of length N, where N may be equal to 4,8, 16, 32, 64 or 128 for cdma2000. In general, orthogonal codes ofdifferent lengths may be used for the traffic channels, and N maycorrespond to the length of the longest orthogonal code.

CDMA modulator 220 includes N traffic channel processors 310 a through310 n for the N traffic channels. Within each traffic channel processor310, a multiplier 312 receives and scales the data symbols for trafficchannel n with a gain g_(i,n) for traffic channel n and provides scaleddata symbols. The gain g_(i,n) may be set to zero if traffic channel nis not used. A Walsh cover unit 314 channelizes the scaled data symbolswith a Walsh code w_(n) assigned to traffic channel n. Unit 314 performscovering by repeating each scaled data symbol multiple times to generateN replicated symbols and then multiplying the N replicated symbols withthe N chips of Walsh code w_(n) to generate N data chips for that datasymbol. A combiner 320 receives and adds the data chips for all Ntraffic channels. A multiplier 322 multiplies the combined data chipswith a spreading code assigned to sector i and generates output chips.

The output chips for sector i may be expressed in discrete time, asfollows:

$\begin{matrix}{{{x_{i}(k)} = {\sum\limits_{n = 1}^{N}{{c_{i}(k)} \cdot {w_{n}\left( {{mod}\left( {k,N} \right)} \right)} \cdot g_{i,n} \cdot {s_{i,n}\left( \left\lfloor {k/N} \right\rfloor \right)}}}},} & {{Eq}\mspace{14mu}(1)}\end{matrix}$where k is an index for chip period,

n is an index for traffic channel,

i is an index for sector,

s_(i,n)(└k/N┘) is a data symbol sent on traffic channel n in chip periodk,

w_(n)(mod(k,N)) is a Walsh chip for traffic channel n in chip period k,

g_(i,n) is the gain for traffic channel n in sector i,

c_(i)(k) is a spreading code chip for sector i in chip period k, and

x_(i)(k) is an output chip for sector i in chip period k.

Each data symbol is sent in N chip periods. Data symbol s_(i,n)(t) forsymbol period t is sent in chip periods k=N·t through N·t+N−1. Hence,t=└k/N┘ and s_(i,n)(t)=s_(i,n)(└k/N┘), where “└x┘” denotes a flooroperator. For simplicity, the data symbols, Walsh chips, and spreadingcode chips are assumed to have unit magnitude for all chip periods k,symbol periods t, traffic channels n, and sector i, or|s_(i,n)(t)|=|w_(n)(mod(k,N))|=|c_(i)(k)|=1 for ∀k,t,n,i. The spreadingcodes for different sectors are uncorrelated, withE{c_(i)(k)·c_(j)*(k+κ)}=δ(κ)·δ(i,j), which means that the expected valuebetween the spreading codes for sectors i and j is equal to one only ifκ=0 and i=j. Different sectors are assigned different shifted versionsof the same PN sequence in cdma2000, in which case the spreading codesfor different sectors are uncorrelated over a range of chip offsets.

Equation (1) may be expressed in matrix form, as follows:x _(i)(t)=C _(i)(t)· W·G _(i) ·s _(i)(t),  Eq (2)

-   where s _(i)(t)=[s_(i,1)(t) s_(i,2)(t) . . . s_(i,N)(t)]^(T) is an    N×1 vector containing N data symbols to be sent on the N traffic    channels in symbol period t,    -   G _(i) is an N×N diagonal matrix containing the gains for the N        traffic channels along the diagonal, or diag (G _(i))={g_(i,1),        g_(i,2), . . . , g_(i,N)},    -   W is an N×N Walsh matrix containing N Walsh codes in N columns,    -   C _(i) (t) is an N×N diagonal matrix containing N spreading code        chips along the diagonal for N chip periods in symbol period t,        or diag (C _(i)(t))={c_(i)(N·t), c_(i)(N·t+1), . . . ,        c_(i)(N·t+N−1)},    -   x _(i)(t)=[x_(i)(N·t)x_(i)(N·t+1) . . . x_(i)(N·t+N−1)]^(T) is        an N×1 vector containing N output chips for sector i in symbol        period t, and    -   “^(T)” denotes a transpose.

A diagonal matrix contains possible non-zero values along the diagonaland zeros elsewhere. If the traffic channels have different Walsh codelengths, then N is equal to the longest Walsh code length for alltraffic channels, and each shorter Walsh code is repeated in matrix W.

Wireless device 120 receives the forward link signals from base station110 i and other base stations. The received samples from receiver 254may be expressed as:

$\begin{matrix}{{{\underset{\_}{r}(t)} = {{\sum\limits_{i}{h_{i} \cdot {{\underset{\_}{x}}_{i}(t)}}} + {\underset{\_}{n}(t)}}},} & {{Eq}\mspace{14mu}(3)}\end{matrix}$where h_(i) is a channel gain for sector i,

n(t) is an N×1 vector of noise and interference not included in x_(i)(t), and

s(t) is an N×1 vector containing N received samples for symbol period t.

Equation (3) assumes that all sectors are synchronized and that there isa single signal path (or no multipath) for each sector. For simplicity,the noise and interference in s(t) may be assumed to be additive whiteGaussian noise (AWGN) with a zero mean vector and a covariance matrix ofN₀·I, where N₀ is the variance of the noise and interference, and I isthe identity matrix with ones along the diagonal and zeros elsewhere.

In equation (3), r(t) is a received vector for one symbol period. Thereceived vectors for different symbol periods are uncorrelated due tothe use of spreading codes that are temporally uncorrelated. Hence,there is no dependence across different symbol periods. For clarity,symbol index t is omitted in much of the description below.

Wireless device 120 may derive estimates of the data symbols transmittedby a given sector j on traffic channel n by (1) despreading the receivedsamples with the spreading code used by sector j and (2) decovering thedespread samples with the Walsh code for traffic channel n, as follows:{hacek over (s)} _(j,n) =w _(n) ^(T) ·C _(j) ^(H) ·r,  Eq (4)where C _(j) is an N×N diagonal matrix containing the spreading codechips for sector j,

w _(n) is an N×1 vector containing the Walsh code for the desiredtraffic channel n,

s_(j,n) is a data symbol sent by sectorj on traffic channel n,

{hacek over (s)}_(j,n) is an estimate of s_(j,n) without interferencecancellation, and

“^(H)” denotes a conjugate transpose.

To cancel the interference from an interfering sector l, wireless device120 may despread the received samples with the spreading code used bysector l and then decover the despread samples, as follows:u _(l) =W ^(T) ·C _(l) ^(H) ·r,  Eq (5)where u _(l) is an N×1 vector containing N received symbols for N Walshbins for sector l. The multiplication by C _(l) ^(H) despreads thereceived samples for sector l. The multiplication by W ^(T) generatesthe received symbols for the N Walsh bins. The N Walsh bins are for Ntraffic channels if these traffic channels are assigned N differentWalsh codes of length N. The N Walsh bins may be viewed as correspondingto N orthogonal channels obtained via the decovering with W ^(T).

A covariance matrix Λ _(l) for vector u _(l) may be expressed as:

$\begin{matrix}\begin{matrix}{{{\underset{\_}{\Lambda}}_{\ell} = {E\left\{ {{\underset{\_}{u}}_{\ell} \cdot {\underset{\_}{u}}_{\ell}^{H}} \right\}}},} \\{{= {{N^{2} \cdot {h_{\ell}}^{2} \cdot {\underset{\_}{G}}_{\ell}^{2}} + {N \cdot \left( {{\sum\limits_{i \neq \ell}{\sum\limits_{n = 1}^{N}{{h_{i}}^{2} \cdot g_{i,n}^{2}}}} + N_{0}} \right) \cdot \underset{\_}{I}}}},} \\{{= {{q_{\ell} \cdot {\underset{\_}{G}}_{\ell}^{2}} + {N \cdot \sigma_{\ell}^{2} \cdot \underset{\_}{I}}}},}\end{matrix} & {{Eq}\mspace{14mu}(6)}\end{matrix}$where q_(l)=N²·|h_(l)|² is a channel power gain for sector l, and

$\sigma_{\ell}^{2} = {{\sum\limits_{i \neq \ell}{\sum\limits_{n = 1}^{N}{{h_{i}}^{2} \cdot g_{i,n}^{2}}}} + N_{0}}$is the noise and interference from other sectors.

The covariance matrix Λ _(l) may be given as diag (Λ _(l))={λ_(l,1),λ_(l,2), . . . , λ_(l,N)}. The diagonal elements of Λ _(l) are measuredpowers (or eigenvalues) for the N Walsh bins. Λ _(l) is equi-diagonal ifall N diagonal elements are equal, or λ_(l,n)=λ_(l) for ∀n.

Wireless device 120 may derive symbol estimates for traffic channel n ofsector j based on various techniques such as a linear minimum meansquare error (LMMSE) technique, a least squares (LS) technique, and soon. Symbol estimates for traffic channel n of sectorj may be derivedbased on the LMMSE technique, as follows:

$\begin{matrix}\begin{matrix}{{{\overset{\hat{\hat{}}}{s}}_{j,n} = {E{\left( {\left. {s_{j,n}^{*} \cdot {\underset{\_}{u}}_{\ell}} \middle| {\underset{\_}{C}}_{j} \right.,{\underset{\_}{C}}_{\ell}} \right)^{H} \cdot {\underset{\_}{\Lambda}}_{\ell}^{- 1} \cdot {\underset{\_}{u}}_{\ell}}}},} \\{= {E\left( {s_{j,n}^{*} \cdot \left( {{{{\underset{\_}{W}}^{T} \cdot {\underset{\_}{C}}_{\ell}^{H}}{\sum\limits_{i}{h_{i} \cdot {\underset{\_}{C}}_{i} \cdot \underset{\_}{W} \cdot {\underset{\_}{G}}_{i} \cdot {\underset{\_}{s}}_{i}}}} +} \right.} \right.}} \\{{\left. {\left. \left. {~~~~~~~}{{\underset{\_}{W}}^{T} \cdot {\underset{\_}{C}}_{\ell}^{H} \cdot \underset{\_}{n}} \right) \middle| {\underset{\_}{C}}_{j} \right.,{\underset{\_}{C}}_{\ell}} \right)^{H} \cdot {\underset{\_}{\Lambda}}_{\ell}^{- 1} \cdot {\underset{\_}{u}}_{\ell}},} \\{{= {h_{j}^{*} \cdot g_{j,n} \cdot {\underset{\_}{w}}_{n}^{T} \cdot {\underset{\_}{C}}_{j}^{H} \cdot {\underset{\_}{C}}_{\ell} \cdot \underset{\_}{W} \cdot \Lambda_{\ell}^{- 1} \cdot {\underset{\_}{u}}_{\ell}}},}\end{matrix} & {{Eq}\mspace{14mu}(7)}\end{matrix}$where {circumflex over (ŝ)}_(j,n) is an LMMSE estimate of s_(j,n).

The LMMSE symbol estimation in equation (7) may be combined withequation (5) and then broken into smaller equations, as follows:

$\begin{matrix}{{{\underset{\_}{r}}_{\ell} = {\frac{1}{{tr}\left( {\underset{\_}{\Lambda}}_{\ell}^{- 1} \right)} \cdot {\underset{\_}{C}}_{\ell} \cdot \underset{\_}{W} \cdot {\underset{\_}{\Lambda}}_{\ell}^{- 1} \cdot {\underset{\_}{W}}^{T} \cdot {\underset{\_}{C}}_{\ell}^{H} \cdot \underset{\_}{r}}},} & {{Eq}\mspace{14mu}(8)} \\{{{\hat{s}}_{j,n} = {{\underset{\_}{w}}_{n}^{T} \cdot {\underset{\_}{C}}_{j}^{H} \cdot {\underset{\_}{r}}_{\ell}}},{and}} & {{Eq}\mspace{14mu}(9)} \\{{{\overset{\hat{\hat{}}}{s}}_{j,n} = {h_{j}^{*} \cdot g_{j,n} \cdot {{tr}\left( {\underset{\_}{\Lambda}}_{\ell}^{- 1} \right)} \cdot {\hat{s}}_{j,n}}},} & {{Eq}\mspace{14mu}(10)}\end{matrix}$

-   where r _(l) is an N×1 vector containing N interference-canceled    samples having the signal component for sector l suppressed,    -   Λ _(l) ⁻¹ is an N×N diagonal matrix given as diag(Λ _(l)        ⁻¹)={λ_(l,1) ⁻¹, λ_(l,2) ⁻¹, . . . , λ_(l,N) ⁻¹},    -   tr(Λ _(l) ⁻¹) is the trace of Λ _(l) ⁻¹, which is the sum the        diagonal elements of Λ _(l) ⁻¹,    -   ŝ_(j,n) is an unweighted LMMSE estimate of s_(j,n), and    -   {circumflex over (ŝ)}_(j,n) is a weighted LMMSE estimate of        s_(j,n).

Equation (8) represents interference cancellation for one interferingsector l. In the description herein, the interference cancellation inequation (8) is referred to as quasi-linear interference cancellation(QLIC). Vector r _(l) contains samples having the interference fromsector l suppressed. Equation (9) indicates that the remaining LMMSEsymbol estimation for s_(j,n) includes simple despread and decoveroperations that are conventionally done by a CDMA receiver, as shown inequation (4). In particular, vector r _(l) is despread with thespreading code for the desired sector j and then decovered with theWalsh code for the desired traffic channel n. Equation (10) shows theLMMSE scaling to obtain the weighted estimate for subsequent decoding.

As shown in equation (6), the diagonal elements of Λ _(l) are determinedin part by the gain matrix G _(l) for interfering sector l. If the gainsfor all N traffic channels of sector l are equal (i.e., g_(l,n)=g_(l)for ∀n), then G _(l)=g_(l)·I and Λ _(l)=η·I, where η is an overall powergain given as η=N/tr(Λ _(l) ⁻¹). The scaling by 1/tr(Λ _(l) ⁻¹) resultsin r _(l) being equal to r if Λ _(l) is equi-diagonal and contains ηalong the diagonal. In this case, the unweighted symbol estimate ŝ_(j,n)from equation (9) is equal to the symbol estimate {hacek over (s)}_(j,n)from equation (4) without interference cancellation. Interferencecancellation is achieved when the gains in matrix G _(l) are not equal,so that traffic channels with larger gains are attenuated more by themultiplication with the inverted covariance matrix Λ _(l) ⁻¹ in equation(8). In effect, equation (8) applies a normalized Walsh bin scaling of Λ_(l) ⁻¹/tr(Λ _(l) ⁻¹), which achieves relative adjustment forinterference suppression while providing unity overall gain on averageover the N Walsh bins.

FIG. 4 shows a block diagram of a single-sector interference canceller260 a, which is an embodiment of interference canceller 260 in FIG. 2.Within interference canceller 260 a, a multiplier 412 multiplies thereceived samples r with a complex-conjugated spreading code c*_(l) forsector l and provides input samples. A serial-to-parallel (S/P)converter 414 forms a vector of N input samples for each symbol periodand provides the N input samples in parallel. A fast Hadamard transform(FHT) unit 416 performs an N-point FHT on the N input samples for eachsymbol period and provides N received symbols for N Walsh bins.

A unit 422 computes the squared magnitude of the received symbol foreach Walsh bin and provides a power value for that Walsh bin. A filter424 averages the power values from multiple symbol periods for eachWalsh bin and provides a power estimate {circumflex over (λ)}_(l,n) forthat Walsh bin. Filter 424 provides estimates of the diagonal elementsof Λ _(l). Filter 424 may be implemented with a finite impulse response(FIR) filter, an infinite impulse response (IIR) filter, or some othertype of filter. Filter 424 may have a time constant that is selected asdescribed below. A unit 426 computes the inverse of the power estimatefor each Walsh bin and provides N inverse power estimates, which areestimates of the diagonal elements of Λ _(l) ⁻¹. A summer 432 sums the Ninverse power estimates and computes the trace of Λ _(l) ⁻¹. A unit 434computes the inverse of the trace of Λ _(l) ⁻¹ and provides the scalingfactor 1/tr(Λ _(l) ⁻¹). A multiplier 436 multiplies each of the Ninverse power estimates from unit 426 with the scaling factor 1/tr(Λ_(l) ⁻¹) and provides N normalized inverse power estimates for the NWalsh bins. Multiplier 436 may also be located after multiplier 446, asindicated by equation (8).

A multiplier 440 obtains N received symbols for the N Walsh bins in eachsymbol period, multiplies the received symbol for each Walsh bin withthe normalized inverse power estimate for that Walsh bin, and provides Nscaled symbols for the N Walsh bins. Units 422 through 440 performprocessing on a per Walsh bin basis. An inverse FHT (IFHT) unit 442performs an N-point IFHT on the N scaled symbols for each symbol periodand provides N output samples for that symbol period. Aparallel-to-serial (P/S) converter 444 serializes the N output samplesfor each symbol period. A multiplier 446 multiplies the output sampleswith the spreading code for sector l and provides theinterference-canceled samples r_(l) for sector l.

In FIG. 4, multiplier 412 performs despreading for sector l, which ismultiplication with C_(l) ^(H) in equation (8). Serial-to-parallelconverter 414 vectorizes the input samples for each symbol period. FHTunit 416 performs decovering for the N traffic channels, which ismultiplication with W ^(T) in equation (8). FHT unit 416 efficientlyprojects the vectorized samples into orthogonal bins using Walsh codesand diagonalizes the covariance matrix Λ _(l). Multiplier 412, converter414, and FHT unit 416 implement equation (5) and provide u _(l). Unit422, filter 424, and unit 426 derive an estimate of Λ _(l) ⁻¹. Summer432 and unit 434 compute the scaling factor 1/tr(Λ _(l) ⁻¹). Multiplier436 normalizes the inverses of the power estimates, which ismultiplication with 1/tr(Λ _(l) ⁻¹) in equation (8). Multiplier 440scales the N Walsh bins based on the normalized inverse power estimatesfor these Walsh bins, which is multiplication with Λ _(l) ⁻¹ in equation(8). Hence, Walsh bins with larger powers are attenuated more, whichreduces the interference contributions from these Walsh bins. IFHT unit442 performs covering for the N Walsh bins, which is multiplication withW in equation (8). Multiplier 446 performs spreading (or respreading)for sector l, which is multiplication with C _(l) in equation (8).

The interference cancellation is performed based on covariance matrix Λ_(l), which has the form shown in equation (6). Interferencecancellation performance is dependent on the ability to accuratelyestimate Λ _(l). In the context of interference cancellation, trackingrefers to the ability to accurately estimate matrix Λ _(l) under varyingoperating environment.

Matrix Λ _(l) may be estimated in various manners. In an embodiment thatis shown in FIG. 4, matrix Λ _(l) is estimated by averaging the powervalues for the received symbols in vector u _(l) across multiple symbolperiods. In this embodiment, filter 424 may be designed to providesuitable averaging for the expected operating environment. In anotherembodiment that is described below, matrix Λ _(l) is estimated based oncomponents of Λ _(l).

From equation (6), the diagonal elements of Λ _(l) may be expressed as:λ_(l,n) =N ² ·|h _(l)|² ·g _(l,n) ² +N·σ _(l) ², for n=1, . . . , N.  Eq(11)

Equation (11) indicates that the power λ_(l,n) of Walsh bin n for sectorl is determined by the channel gain h_(l) for sector l, the gain g_(l,n)for traffic channel n in sector l, and the noise and interference σ_(l)² for sector l. Gain g_(l,n) is also referred to as the bin gain forWalsh bin n. The channel gain h_(l) and the noise and interference σ_(l)² are common for all N Walsh bins. The channel gain h_(l) may beestimated based on a pilot transmitted by sector l using any channelestimation scheme known in the art. The channel gain estimate for sectorl is denoted as ĥ_(l).

In an embodiment, the noise and interference σ_(l) ² for sector l isestimated based on a power estimate for a Walsh bin corresponding to anunused traffic channel. An unused traffic channel has a gain of zero, org_(l,n)=0. In this case, the power of the corresponding null Walsh bincontains only the noise and interference, or λ_(l,null)=N·σ_(l) ², whereλ_(l,null) is the power of the null Walsh bin. The noise andinterference estimate may be set equal to the power estimate for thenull Walsh bin, as follows:N·{circumflex over (σ)} _(l) ²={circumflex over (λ)}_(l,null),  Eq (12)where {circumflex over (λ)}_(l,null) is an estimate of λ_(l,null) (e.g.,from filter 424 in FIG. 4) and {circumflex over (σ)}_(l) ² is anestimate of σ_(l) ². An unused traffic channel may be identified basedon signaling from a sector, the structure of the traffic channels, andso on. For example, if the processing for interference cancellation isperformed in intervals of multiple symbols (e.g., 2N or 4N) and if thepilot is transmitted with a Walsh code of all zeros, then an unusedtraffic channel corresponding to a sub-branch of the pilot Walsh codemay be used for noise and interference estimation.

In another embodiment, the noise and interference σ_(l) ² for sector lis estimated based on the smallest power estimate for all Walsh bins forsector l, as follows:

$\begin{matrix}{{N \cdot {\hat{\sigma}}_{l}^{2}} = {\min\limits_{n}{\left\{ {\hat{\lambda}}_{l,n} \right\}.}}} & {{Eq}\mspace{14mu}(13)}\end{matrix}$The smallest power estimate may be assumed to be for an unused trafficchannel. Since λ_(l,n) is a random variable, setting {circumflex over(94 )}_(l) ² to the smallest power estimate results in {circumflex over(σ)}_(l) ² having a negative bias and under-estimating the noise andinterference. A scaling factor may be used to account for the negativebias.

In yet another embodiment, the noise and interference σ_(l) ² for sectorl is estimated based on an average of a predetermined number of smallestpower estimates for all Walsh bins for sector l. The noise andinterference may also be estimated in other manners.

In an embodiment, the bin gain g_(l,n) is estimated based on the powerestimate for Walsh bin n and a pilot power estimate, as follows:

$\begin{matrix}{{{\hat{g}}_{\ell,n}^{2} = \frac{{\hat{\lambda}}_{\ell,n} - {N \cdot {\hat{\sigma}}_{\ell}^{2}}}{{\hat{\lambda}}_{\ell,{pilot}} - {N \cdot {\hat{\sigma}}_{\ell}^{2}}}},} & {{Eq}\mspace{14mu}(14)}\end{matrix}$where {circumflex over (λ)}_(l,n) is an estimate of the power of Walshbin n,

{circumflex over (λ)}_(l,pilot) is an estimate of the power of the Walshbin for the pilot, and

{circumflex over (λ)}_(l,n) is an estimate of bin gain g_(l,n).

Equation (14) gives a bin gain estimate relative to (or normalized by)the pilot channel gain. For interference cancellation, it is sufficientto have the bin gain estimates be proportionally correct.

The channel gain estimate ĥ_(l), the noise and interference estimate{circumflex over (σ)}_(l) ², and the bin gain estimate ĝ_(l,n) may bederived with filters selected to provide good estimation performance.Each component may be derived with a respective filter having a timeconstant that is selected to provide an accurate estimate for thatcomponent. In general, a longer time constant provides better averagingover random fluctuations of estimation errors but has poorer ability totrack rapid changes in the environment. The converse is true for ashorter time constant. A shorter time constant may be used for thechannel gain estimate ĥ_(l) in order to accommodate fast fading. Alonger time constant may be used for the bin gain estimate ĝ_(l,n) andmay be selected to track changes in traffic channel gain due to powercontrol, which may be 0.5 decibel (dB) per 1.25 milliseconds (ms) incdma2000. A shorter time constant may also be used for the noise andinterference estimate {circumflex over (σ)}_(l) ² in order toaccommodate rapid changes in the channel gains h_(i) for the othersectors due to fast fading. The time constants for the differentcomponents may be selected based on computer simulation, empiricalmeasurements, and so on.

The elements of matrix Λ _(l) may be derived based on the channel gainestimate, the noise and interference estimate, and the bin gainestimates, as follows:{circumflex over ({circumflex over (λ)}_(l,n) =N ² ·|ĥ _(l)|² ·ĝ _(l,n)² +N·{circumflex over (σ)} _(l) ²,  Eq (15)where {circumflex over ({circumflex over (λ)}_(l,n) is an improved powerestimate for Walsh bin n of sector l, which is derived based onestimates of the components of λ_(l,n). A matrix {circumflex over({circumflex over (Λ)}_(l) may be formed with the N power estimates{circumflex over ({circumflex over (λ)}_(l,n), for n=1, . . . , N, forthe N Walsh bins and may be used for interference cancellation.

FIG. 5 shows an embodiment of a process 500 for performing interferencecancellation. Time-domain receive samples (e.g., for CDMA) orfrequency-domain received samples (e.g., for OFDM) are initiallyobtained. The received samples are processed to isolate the signal froman interfering transmitter l (block 512). The processing in block 512may be an operation such as despreading for cdma2000, descrambling forW-CDMA, and so on. Decomposition is then performed to obtain multipleorthogonal bins for transmitter l (block 516). The orthogonal bins mayalso be referred to as orthogonal channels, Walsh bins, eigenmodes,modes, traffic channels, physical channels, and so on. Orthogonal binsare obtained for different Walsh codes in cdma2000 and for differentOVSF codes in W-CDMA. The decomposition may be achieved with an FHT forcdma2000 and W-CDMA, a fast Fourier transform (FFT) for OFDM and FDMAsystems, and with other types of transform for other systems.

Interference cancellation may be achieved by performing LMMSE scalingfor each orthogonal bin. In this case, the power of each orthogonal binfor transmitter l is estimated (block 522). The inverse of the powerestimate for each orthogonal bin is computed (block 526). Eachorthogonal bin is then scaled by the inverse power estimate for thatorthogonal bin, so that orthogonal bins with larger power estimates areattenuated more (block 540). The orthogonal bins are then transformedback to discrete time using the inverse of the transform used fordecomposition (block 542). The processing to isolate transmitter l isthen undone (block 546). The processing in block 546 may be an operationsuch as spreading for cdma2000, scrambling for W-CDMA, and so on.

FIG. 6A shows an embodiment of a process 600 for performing interferencecancellation. Power estimates for multiple orthogonal bins are derivedby estimating at least two components of the power estimates, asdescribed below (block 610). Interference cancellation is then performedusing the power estimates for the multiple orthogonal bins (block 620).Block 610 may correspond to block 522 in FIG. 5, and block 620 mayinclude the remaining blocks in FIG. 5.

FIG. 6B shows an embodiment of block 610 in FIG. 6A. A channel gainestimate ĥ_(l) is derived for a communication channel, e.g., based on areceived pilot (block 632). Initial power estimates {circumflex over(λ)}_(l,n) are derived for the orthogonal bins, e.g., based on thereceived symbols for these bins (block 634). A noise and interferenceestimate {circumflex over (σ)}_(l) ² may be derived based on the initialpower estimate {circumflex over (λ)}_(l,null) for a null orthogonal bin,the smallest initial power estimate for all orthogonal bins, and so on(block 636). A bin gain estimate ĝ_(l,n) may be derived for eachorthogonal bin, e.g., based on the initial power estimate {circumflexover (λ)}_(l,n) for that orthogonal bin, a pilot power estimate{circumflex over (λ)}_(l,pilot), and the noise and interference estimate{circumflex over (σ)}_(l) ², as shown in equation (14) (block 638).Filters with the same or different time constants may be used for thethree components ĥ_(l), {circumflex over (σ)}_(l) ² and ĝ_(l,n). Thechannel gain estimate ĥ_(l) and the noise and interference estimate{circumflex over (σ)}_(l) ² are common for all orthogonal bins. A powerestimate {circumflex over ({circumflex over (λ)}_(l,n) may then bederived for each orthogonal bin based on the channel gain estimateĥ_(l), the noise and interference estimate {circumflex over (σ)}_(l) ²,and the bin gain estimate ĝ_(l,n) for that orthogonal bin, e.g., asshown in equation (15) (block 640).

In the embodiment shown in FIG. 6, the channel gain, the noise andinterference, and the bin gains are three components that are estimatedseparately and then combined to derive the power estimates for theorthogonal bins. In other embodiments, other combination of componentsmay be estimated separately and used to derive the power estimates. Forexample, the channel gain and the bin gains may be estimated together.

FIGS. 4 through 6B show interference cancellation for one interferingsector l. Interference from multiple sectors may also be estimated andcanceled prior to demodulating a desired sector.

A cancellation term e _(l) for each sector l may be defined as:e _(l) =r−r _(l).  Eq (16)

Vector e _(l) contains the signal component for sector l as well asdistortion noise due to the σ_(l) ² term in equation (6). Vector e _(l)represents an interference component for other sectors and is equal tozero if Λ _(l), is equi-diagonal. Vectors e _(l), for different sectorsare uncorrelated due to the use of different spreading codes bydifferent sectors. Vector e _(l) for an interfering sector l is alsouncorrelated with transmitted vector x _(j) for a desired sector j,again due to the use of different spreading codes. The scaling factor1/tr(Λ _(l) ⁻¹) in equation (8) for r _(l) results in optimal weightingof the interference contributions from different interfering sectors.

An estimate of the transmitted vector x _(j) for desired sector j may beexpressed as:

$\begin{matrix}{{{\underset{\_}{\hat{x}}}_{j} = {{\underset{\_}{r} - {\sum\limits_{\ell \neq j}{\underset{\_}{e}}_{\ell}}} = {\underset{\_}{r} - {\underset{\_}{e}}_{{os},j}}}},} & {{Eq}\mspace{14mu}(17)}\end{matrix}$where {circumflex over (x)} _(j) is an estimate of x _(j), and e _(os,j)is the sum of the cancellation signals from the other sectors. Vector{circumflex over (x)} _(j) includes the signal component from desiredsector j and has the interference components from the other sectorscanceled. Equations (16) and (17) maximize thesignal-to-noise-and-interference ratio (SINR) of vector {circumflex over(x)} _(j) under an assumption that the data symbols from each sector areindependent and zero mean.

Vector {circumflex over (x)} _(j) may be despread and decovered toobtain data symbol estimates for a desired traffic channel n fromdesired sector j, as follows:ŝ _(j,n) =w _(n) ^(T) ·C _(j) ^(H) ·{circumflex over (x)} _(j).  Eq (18)

FIG. 7 shows a block diagram of a parallel multi-sector interferencecanceller 260 b, which is another embodiment of interference canceller260 in FIG. 2. Interference canceller 260 b performs interferencecancellation for multiple (L) sectors and provides estimates of thesignals transmitted by these L sectors.

Within interference canceller 260 b, the received signal r (whichcorresponds to the received samples from receiver 254) is provided to LQLIC blocks 710 a through 7101 for the L sectors. Each QLIC block 710derives a cancellation signal for its assigned sector and may beimplemented as described below. A combiner 720 sums the cancellationsignals e₁ through e_(L) for all L sectors and provides a totalcancellation signal e_(total). For each sector j, a summer 712 subtractsthe cancellation signal e_(j) for that sector from the totalcancellation signal e_(total) and provides an other-sector cancellationsignal e_(os,j), which corresponds to the term

$\sum\limits_{\ell \neq j}{\underset{\_}{e}}_{\ell}$in equation (17). For each sector j, a summer 714 subtracts theother-sector cancellation signal e_(os,j) for that sector from thereceived signal r and provides a signal estimate {circumflex over(x)}_(j) for the sector. The signal estimate {circumflex over (x)}_(j)for each sector has the cancellation signals from the other L−1 sectorsremoved. Summers 714 a through 714 l provide the signal estimates{circumflex over (x)}₁ through {circumflex over (x)}_(L) for the Lsectors to L finger processors 750 a through 750 l, respectively, withinrake receiver 270. Each finger processor 750 may perform demodulation asshown in equation (18) for its assigned sector.

FIG. 7 shows an embodiment of interference cancellation for multiplesectors in parallel. The cancellation signals for the L sectors arederived in parallel based on the received signal r. The accuracy of thecancellation signal for each sector is affected by the interference fromall other sectors. The signal estimate {circumflex over (x)}_(j) foreach sector is derived based on the cancellation signal e_(j) for thatsector, the total cancellation signal e_(total) for all L sectors, andthe received signal r.

Interference cancellation for multiple sectors may also be performed ina successive manner, i.e., a sequential or cascaded manner. Successiveinterference cancellation for L sectors may be performed in L successivestages, with each stage canceling the interference from one sector. Theinterference cancellation at each stage may be performed based on theoutput from a preceding stage, which may have the interference from allprior stages removed and may thus be “cleaner” than the received signal.Successive interference cancellation may improve performance. Forexample, if different sectors cause different amounts of interference,then interference cancellation may first be performed for a strongsector to suppress the signal components from this sector and may thenbe performed for a weaker sector. The interference cancellation for theweaker sector may improve because the signal contributions from thestrong sector have been attenuated. The cancellation of the strongsector reduces the σ_(l) ² term in equation (6) for the weaker sector,which makes the gain matrix G _(l) for the weaker sector more prominentand improves the characteristics of the covariance matrix Λ _(l) for theweaker sector. Hence, cancellation of the strong sector may improveinterference cancellation for the weaker sector.

FIG. 8A shows a block diagram of a cascaded two-sector interferencecanceller 260 c, which is yet another embodiment of interferencecanceller 260 in FIG. 2. In this embodiment, the interference fromsector a is first canceled, and the interference from sector b is thencanceled to generate a signal estimate for desired sector j.

Within interference canceller 260 c, the received signal r is providedto a QLIC block 810 a, which derives a cancellation signal e_(a) ¹ forsector a. The superscript ‘1’ in e_(a) ¹ is for the stage number, andthe subscript a is for the sector being processed by the stage. A summer812 a subtracts the cancellation signal e_(a) ¹ from the received signalr and provides an intermediate signal r¹ having the signal component anddistortion noise for sector a suppressed. A QLIC block 810 b receivesthe intermediate signal r¹ and derives a cancellation signal e_(b) ² forsector b. A summer 812 b subtracts the cancellation signal e_(b) ² fromthe received signal r and provides a signal estimate {circumflex over(x)}_(j) containing the signal component for desired sector j but havingthe interference from sectors a and b suppressed. A finger processor 750j within rake receiver 270 performs demodulation on the signal estimate{circumflex over (x)}_(j) for desired sector j.

Sector a may be desired sector j or another sector. If sector a isdesired sector j, then the signal component for the desired sector isfirst canceled, which may improve the cancellation of the interferencefrom sector b in the second stage.

FIG. 8B shows a block diagram of a cascaded multi-sector interferencecanceller 260 d, which is yet another embodiment of interferencecanceller 260 in FIG. 2. In this embodiment, the signal components for Lsectors are successively suppressed in L stages.

Within interference canceller 260 d, the received signal r is providedto QLIC block 810 a, which derives a cancellation signal e_(a) ¹ forsector a. Summer 812 a subtracts the cancellation signal e_(a) ¹ fromthe received signal r and provides an intermediate signal r¹ having thesignal component for sector a suppressed. QLIC block 810 b receives theintermediate signal r¹ and derives a cancellation signal e_(b) ² forsector b. A summer 812 b subtracts the cancellation signal e_(b) ² fromthe intermediate signal r¹ and provides an intermediate signal r² havingthe signal components for both sectors a and b suppressed.

Each subsequent stage i operates in similar manner as stage 2. QLICblock 810 for stage i receives the intermediate signal r^(i−1) fromprior stage i−1 and derives a cancellation signal e_(i) ^(i) for sectori assigned to stage i. Summer 812 for stage i subtracts the cancellationsignal e_(i) ^(i) from the intermediate signal r^(i−1) and provides tothe next stage an intermediate signal r^(i) having the signal componentsfor all sectors assigned to the current and prior stages suppressed.

Summer 8121 for the last stage provides an intermediate signal r^(L)having the signal components from all L sectors suppressed. For eachsector i, for i=1, . . . , L−1, a summer 814 adds the cancellationsignal e_(i) ^(i) for sector i with the intermediate signal r^(L) andprovides a signal estimate {circumflex over (x)}_(i) for that sector.The intermediate signal r^(L−1) has the interference from sectors 1through L−1 suppressed and is provided as the signal estimate{circumflex over (x)}_(L) for sector L.

In an embodiment, the sectors are assigned to the stages based on theirsignal strength. For example, the strongest received sector may beassigned to stage 1, the next strongest received sector may be assignedto stage 2, and so on. In another embodiment, the sector with theearliest arriving signal may be assigned to stage 1, the sector with thenext arriving signal may be assigned to stage 2, and so on. The sectorsmay also be assigned to the stages in other manners.

FIG. 9 shows a block diagram of a parallel two-stage interferencecanceller 260 e, which is yet another embodiment of interferencecanceller 260 in FIG. 2. Interference canceller 260 e is a combinationof interference canceller 260 b in FIG. 7 and interference canceller 260c in FIG. 8A.

In the first stage, the received signal r is provided to L QLIC blocks910 a through 910 l for L sectors. Each QLIC block 910 derives acancellation signal for its assigned sector based on the receivedsignal. A combiner 920 a sums the cancellation signals e₁ ¹ throughe_(L) ¹ from all L QLIC blocks 910 a through 910 l and provides a totalcancellation signal e_(total) ¹ for the first stage. For each sector j,a summer 912 subtracts the cancellation signal e_(j) ¹ for that sectorfrom the total cancellation signal e_(total) ¹ and provides another-sector cancellation signal e_(os,j) ¹ for the sector. For eachsector j, a summer 914 subtracts the other-sector cancellation signale_(os,j) ¹ from the received signal r and provides an initial signalestimate {circumflex over (x)}_(j) ¹ for the sector. The initial signalestimate for each sector has the cancellation signals from the other L−1sectors removed. Summers 914 a through 914 l provide the initial signalestimates {circumflex over (x)}₁ ¹ through {circumflex over (x)}_(L) ¹for the L sectors.

For the second stage, QLIC blocks 930 a through 930 l receive theinitial signal estimates {circumflex over (x)}₁ ¹ through {circumflexover (x)}_(L) ¹, respectively. Each QLIC block 930 derives acancellation signal e_(j) ² for its assigned sectorj based on itsinitial signal estimate {circumflex over (x)}_(j) ¹. For each sector j,the cancellation signal e_(j) ² from the second stage is typically abetter estimate of the signal component for sector j than thecancellation signal e_(j) ¹from the first stage because e_(j) ² isderived based on the initial signal estimate {circumflex over (x)}_(j) ¹having the interference from the other L−1 sectors suppressed. Acombiner 920 b sums the cancellation signals e₁ ² through e^(L) ² fromall L QLIC blocks 930 a through 930 l and provides a total cancellationsignal e_(total) ² for the second stage. For each sector j, a summer 932subtracts the cancellation signal e_(j) ² for that sector from the totalcancellation signal e_(total) ² and provides an other-sectorcancellation signal e_(os,j) ² for the sector. For each sector j, asummer 934 subtracts the other-sector cancellation signal e_(os,j) ²from the received signal r and provides a final signal estimate{circumflex over (x)}_(j) for the sector. The final signal estimate{circumflex over (x)}_(j) for each sector has the signal components fromthe other L−1 sectors suppressed. Summers 934 a through 934 l providethe final signal estimates {circumflex over (x)}₁ through {circumflexover (x)}_(L) for the L sectors to L finger processors 750 a through 750l, respectively, within rake receiver 270.

FIGS. 7 through 9 show some interference cancellers that performinterference cancellation for one or multiple sectors. Each QLIC blockin FIGS. 7 through 9 may derive a cancellation signal for one signalpath of one sector (per path processing), for multiple signal paths ofone sector (per sector processing), or for multiple signal paths ofmultiple sectors (multi-sector processing). The multiple signal pathsprocessed by a given QLIC block may be for one or multiple receiveantennas. Other interference cancellers may also be designed based onthe description provided herein. For example, the embodiment shown inFIG. 9 may be extended to include more than two cascaded interferencecancellation stages.

FIG. 10A shows a block diagram of a QLIC block 1010 a, which may be usedfor each QLIC block in interference cancellers 260 b through 260 e inFIGS. 7 through 9. For clarity, FIG. 10A shows QLIC block 1010 a beingused in the first stage, so that the incoming samples are the receivedsamples for the received signal r. QLIC block 1010 a includes all of theunits in interference canceller 260 a in FIG. 4. QLIC block 1010 afurther includes a summer 448 that subtracts the interference-canceledsamples r_(l) from the received samples r and provides the cancellationsamples e_(l) for sector l.

FIG. 10B shows a block diagram of a QLIC block 1010 b, which may also beused for each QLIC block in interference cancellers 260 b through 260 e.QLIC block 1010 b performs resampling of the incoming samples to theproper chip timing. Hence, QLIC block 1010 b may be used in interferencecancellers 260 b through 260 e even if the sectors are unsynchronizedand the signals from these sectors are received not aligned in time atthe wireless device. QLIC block 1010 b includes units 410 and 450 inaddition to all of the units in interference canceller 260 a in FIG. 4.Unit 410 performs resampling (e.g., interpolation) on the incomingsamples based on the timing of sector l to synchronize with chip timing.Unit 410 may obtain the received samples at twice the chip rate (orchip×2) and may generate interpolated samples at chip rate (or chip×1)and with the timing of sector l. Unit 450 performs extrapolation on thesamples from summer 448 and provides cancellation samples at the samerate and with the same timing as the incoming samples.

In FIGS. 7 through 9, each QLIC block may operate based on the timing ofthe sector assigned to that QLIC block. The extrapolation by unit 450aligns the timing of the cancellation samples for all sectors so thatthese samples can be summed by combiners 720, 920 a and 920 b.

The interference cancellation in each QLIC block is performed based onthe covariance matrix Λ _(l), which may be estimated (1) based on thereceived symbols in vector u _(l) as shown in FIGS. 4, 10A and 10B, or(2) by estimating the components of Λ _(l) as described above. Theestimation of matrix Λ _(l) may be performed with one or more filtershaving one or more time constants selected to provide good estimationperformance and to track changes in the operating environment. Thesechanges may include changes to the wireless channel response h_(i),changes to the traffic channel gain g_(i,n) due to power control (e.g.,0.5 dB per 1.25 ms in cdma2000), instantaneous changes of the trafficchannels at frame boundaries due to changes in data rates and/or trafficchannel assignment, and/or other changes. It is desirable to obtain anaccurate estimate of Λ _(l) while quickly adapting to changes in theenvironment. Such robust tracking capability may improve interferencecancellation performance.

Tracking is more challenging for a cascaded interference canceller withmultiple stages because of latency introduced by each stage. Trackingperformance for a cascaded interference canceller may be improved asdescribed below.

For a cascaded interference canceller with M stages, where M≧2, theinterference-canceled signal for each sector in each stage may beexpressed as:

$\begin{matrix}{{{{\underset{\_}{r}}_{\ell}^{m} = {\frac{1}{{tr}\left( {\underset{\_}{\Lambda}}_{\ell,m}^{- 1} \right)} \cdot {\underset{\_}{C}}_{\ell} \cdot \underset{\_}{W} \cdot {\underset{\_}{\Lambda}}_{\ell,m}^{- 1} \cdot {\underset{\_}{W}}^{T} \cdot {\underset{\_}{C}}_{\ell}^{H} \cdot {\underset{\_}{\hat{x}}}_{\ell}^{m - 1}}},\;{{{for}\mspace{11mu} m} = 1},\ldots\mspace{11mu},M,}\;} & {{Eq}\mspace{14mu}(19)}\end{matrix}$where {circumflex over (x)} _(l) ^(m−1) is the incoming signal forsector l in stage m,

r _(l) ^(m) is the interference-canceled signal for sector l in stage m,and

Λ _(l,m) is the covariance matrix for sector l in stage m.

As shown in FIG. 9, the incoming signal is equal to the received signalfor the first stage, or {circumflex over (x)} _(l) ⁰=r, and is equal tothe output of the prior stage for each subsequent stage.

From equation (19), the received symbols for each sector in each stagemay be expressed as:u _(l) ^(m) =W ^(T) ·C _(l) ^(H) ·{circumflex over (x)} _(l) ^(m−1), form=1, . . . , M,  Eq (20)where u _(l) ^(m) is the received symbols for sector l in stage m.

The cancellation signal for each sector in each stage may be expressedas:e _(l) ^(m) ={circumflex over (x)} _(l) ^(m−1) −r _(l) ^(m), for m=1, .. . , M,  Eq (21)where e _(l) ^(m) is the cancellation signal for sector l in stage m.

The signal estimate for each sector in each stage may be expressed as:

$\begin{matrix}\begin{matrix}{{\underset{\_}{\hat{x}}}_{l}^{m} = {\underset{\_}{r} - {\sum\limits_{i \neq l}{\underset{\_}{e}}_{i}^{m}}}} \\{{= {\underset{\_}{r} - {\underset{\_}{e}}_{{os},l}^{m}}},} \\{{{{for}\mspace{14mu} m} = 1},\ldots\mspace{11mu},M,}\end{matrix} & {{Eq}\mspace{14mu}(22)}\end{matrix}$where {circumflex over (x)} _(l) ^(m) is the signal estimate for sectorl in stage m, and

e _(os,l) ^(m) is the other-sector cancellation signal for sector l instage m.

The covariance matrix Λ _(l,m) for each sector in each stage may beexpressed as:

$\begin{matrix}\begin{matrix}{{{\underset{\_}{\Lambda}}_{l,m} = {E\left\{ {{\underset{\_}{u}}_{l}^{m} \cdot \left( {\underset{\_}{u}}_{l}^{m} \right)^{H}} \right\}}},} \\{{= {E\left\{ {{\underset{\_}{W}}^{T} \cdot \underset{\_}{C_{l}^{H}} \cdot {\underset{\_}{\hat{x}}}_{l}^{m - 1} \cdot \left( {\hat{\underset{\_}{x}}}_{l}^{m - 1} \right)^{H} \cdot {\underset{\_}{C}}_{l} \cdot \underset{\_}{W}} \right\}}},} \\{{= {{N^{2} \cdot {h_{l}}^{2} \cdot {\underset{\_}{G}}_{l}^{2}} + {N \cdot \sigma_{l,m}^{2} \cdot \underset{\_}{I}}}},}\end{matrix} & {{Eq}\mspace{14mu}(23)}\end{matrix}$where σ_(l,m) ² is the noise and interference for sector l in stage m.

In the first stage, the interference in the incoming signal has not beensuppressed, and σ_(l,m) ² may be given as:

$\begin{matrix}{\sigma_{l,1}^{2} = {{\sum\limits_{i \neq l}{\sum\limits_{n = 1}^{N}{{h_{i}}^{2} \cdot g_{i,n}^{2}}}} + {N_{0}.}}} & {{Eq}\mspace{14mu}(24)}\end{matrix}$In equation (24), N₀ is the noise component and the double summation isfor the interference components from other sectors. In each subsequentstage, the interference components in σ_(l,m) ² are reduced by theinterference cancellation in prior stage(s).

Equations (23) and (24) indicate that σ_(l,m) ² and hence Λ _(l,m) aredependent on the QLIC operation in the previous stages. Each stage mayestimate Λ _(l,m) with a filter, e.g., as shown in FIG. 10A or 10B, andmay use the estimate of Λ _(l,m) to derive the cancellation signal e_(l) ^(m) for that stage. The filter in each stage introduces latency.Since the output {circumflex over (x)} _(l) ^(m) of one stage isprovided as the input of the next stage, the filter latency in eachstage ripples to the next stage. The total latency at any given stage isequal to the accumulated latency for all stages from the first stage tothat stage. Latency may be problematic for a cascaded interferencecanceller.

Accelerated tracking for subsequent stages may be achieved by exploitingthe structure of the covariance matrix Λ _(l,m). Equation (23) indicatesthat Λ _(l,m) is a sum of two terms: a first term N²·|h_(l)|²·G _(l) ²that is common for all stages and a second term N·σ_(l,m) ²·I that isdifferent for different stages (typically smaller for later stages). Anaccurate estimate of Λ _(l,m) may be derived in each stage by exploitingthis structure.

The covariance matrices for stages 1 and m may be expressed as:Λ _(l,1) =N ² ·|h _(l)|² ·G _(l) ² +N·σ _(l,1) ² ·I , and  Eq (25)Λ _(l,m) =N ² ·|h _(l)|² ·G _(l) ² +N·σ _(l,m) ² ·I.   Eq (26)

The two covariance matrices may be combined as follows:Λ _(l,m)−Λ _(l,1) =N·σ _(l,m) ² ·I−N·σ _(l,1) ² ·I.  Eq (27)

Rearranging the terms in equation (26), Λ _(l,m) may be expressed as:Λ _(l,m)=Λ _(l,1)+(σ_(l,m) ²−σ_(l,1) ²)·N·I.  Eq (28)Equation (28) indicates that Λ _(l,m) for stage m may be derived basedon Λ _(l,m) for stage 1 and a scalar indicative of the differencebetween σ_(l,m) ² and σ_(l,1) ².

The N elements of Λ _(l,m) for each stage may be summed as follows:tr(Λ _(l,m))=N ² ·|h _(l)|² ·tr(G _(l) ²)+N ²·σ_(l,m) ².  Eq (29)

Rearranging the terms in equation (29), σ_(l,m) ² may be expressed as:

$\begin{matrix}{\sigma_{l,m}^{2} = {\frac{{{tr}\left( {\underset{\_}{\Lambda}}_{l,m} \right)} - {N^{2} \cdot {h_{l}}^{2} \cdot {{tr}\left( {\underset{\_}{G}}_{l}^{2} \right)}}}{N^{2}}.}} & {{Eq}\mspace{14mu}(30)}\end{matrix}$

Substituting equation (30) into equation (28), Λ _(l,m) may be expressedas:

$\begin{matrix}{{\underset{\_}{\Lambda}}_{l,m} = {{\underset{\_}{\Lambda}}_{l,1} + {\frac{{{tr}\left( {\underset{\_}{\Lambda}}_{l,m} \right)} - {{tr}\left( {\underset{\_}{\Lambda}}_{l,1} \right)}}{N} \cdot {\underset{\_}{I}.}}}} & {{Eq}\mspace{14mu}(31)}\end{matrix}$

Equation (31) indicates that Λ _(l,m) may be obtained based on Λ _(l,1)and the traces of Λ _(l,m) and Λ _(l,1). The trace of Λ _(l,m) may beestimated much faster than Λ _(l,m) can be estimated due to the factthat the trace is a sum over N Walsh bins, which improves reliabilitydue to the averaging over the N Walsh bins. As an example, if N=128,then the trace of Λ _(l,m) may be estimated 128 times faster than Λ_(l,m) for a given estimation accuracy.

An estimate of Λ _(l,m) may be derived in each stage after the firststage, as follows:

$\begin{matrix}{{{\underset{\_}{\hat{\Lambda}}}_{l,m} = {{\underset{\_}{\hat{\Lambda}}}_{l,1} + {\frac{{\hat{S}}_{l,m} - {\hat{S}}_{l,1}}{N} \cdot \underset{\_}{I}}}},} & {{Eq}\mspace{14mu}(32)}\end{matrix}$where S_(l,m)=tr(Λ _(l,m)) is the total power for all N Walsh bins forsector l in stage m,

Ŝ_(l,m) is an estimate of S_(l,m), and

{circumflex over (Λ)} _(l,m) is an estimate of Λ _(l,m).

In equation (32), {circumflex over (Λ)} _(l,m) may be set to zero if thequantity on the right hand side is less than zero, since the noise plusinterference cannot be less than zero.

FIG. 11 shows a block diagram of a multi-stage interference canceller260 f, which is yet another embodiment of interference canceller 260 inFIG. 2. Interference canceller 260 f performs interference cancellationfor one or more sectors in M stages, where M≧2. For simplicity, onlyunits pertinent for interference cancellation for one sector l is shownin FIG. 11 and described below.

In the first stage, a QLIC block 1110 derives a cancellation signale_(l) ¹ for sector l based on the received signal r. QLIC block 1110also derives and provides a total power estimate Ŝ_(l,1) and per-binpower estimates {circumflex over (Λ)} _(l,1) for the first stage. Acombiner 1120 a sums the cancellation signals from all QLIC blocks inthe first stage and provides a total cancellation signal e_(total) ¹ forthe first stage. A combiner 1112 derives a signal estimate {circumflexover (x)}_(l) ¹ for sector l based on the received signal r, thecancellation signal e_(l) ¹ for sector l, and the total cancellationsignal e_(total) ¹. Combiner 1112 may be implemented with summers 912and 914 in FIG. 9.

In each subsequent stage m, where 1<m≦M, a QLIC block 1130 derives acancellation signal e_(l) ^(m) for sector l based on an incoming signal{circumflex over (x)}_(l) ^(m−1) (which is the output from the priorstage m−1) and the power estimates Ŝ_(l,1) and {circumflex over (Λ)}_(l,1) from QLIC block 1110 in the first stage. A combiner 1120 sums thecancellation signals from all QLIC blocks in stage m and provides atotal cancellation signal e_(total) ^(m) for stage m. A combiner 1132derives a signal estimate {circumflex over (x)}_(l) ^(m) for sector lbased on the received signal r, the cancellation signal e_(l) ^(m) forsector l, and the total cancellation signal e_(total) ^(m). Combiner1132 m for the last stage provides the final signal estimate {circumflexover (x)}_(l) ^(M) for sector l to finger processor 7501 within rakereceiver 270.

FIG. 12A shows a block diagram of an embodiment of QLIC block 1110,which may be used in the first stage of a cascaded interferencecanceller such as the one shown in FIG. 11. QLIC block 1110 includes asummer 462 and a filter 464 in addition to all of the units in QLICblock 1010 a in FIG. 10A. In each symbol period, unit 422 provides Npower values that are the squared magnitude of the received symbols forthe N Walsh bins. Summer 462 sums the N power values from unit 422 ineach symbol period and provides a total power value for that symbolperiod. Filter 464 filters the output of summer 462 with a fast timeconstant and provides the total power estimate Ŝ_(l,1) for the firststage. Filter 424 filters the N power values from unit 422 with a slowtime constant and provides N per-bin power estimates {circumflex over(λ)}_(l,1,1) through {circumflex over (λ)}_(l,N,1) for the N Walsh bins,which are the N diagonal elements of matrix {circumflex over (Λ)} _(l,1)for the first stage. The other units within QLIC block 1110 operate asdescribed above for FIGS. 4 and 10A.

FIG. 12B shows a block diagram of an embodiment of QLIC block 1130,which may be used in each stage after the first stage of a cascadedinterference canceller. QLIC block 1130 includes all of the units inQLIC block 1110 except for filter 424. QLIC block 1130 further includessummers 466 and 470 and a divider 468.

Within QLIC block 1130, summer 462 and filter 464 derive a total powerestimate Ŝ_(l,m) for stage m, as described above for FIG. 12A. Summer466 receives the total power estimate Ŝ_(l,1) for the first stage andsubtracts Ŝ_(l,1) from Ŝ_(l,m). Divider 468 divides the output of summer466 by N and provides the quantity (Ŝ_(l,m)−Ŝ_(l,1))/N. Summer 470receives the per-bin power estimates {circumflex over (λ)}_(l,1,1)through {circumflex over (λ)}_(l,N,1) in matrix {circumflex over (Λ)}_(l,1) from QLIC block 1110 for the first stage, sums each per-bin powerestimate {circumflex over (λ)}_(l,n,1) with the output of divider 468,and provides N per-bin power estimates {circumflex over (λ)}_(l,1,m)through {circumflex over (λ)}_(l,N,m) for the N Walsh bins, which arethe N diagonal elements of matrix {circumflex over (Λ)} _(l,m) for stagem. As shown in FIG. 12B, the per-bin power estimates for subsequentstage m may be derived with just filter 464, and filter 424 is notneeded. Units 426 through 440 operate on the per-bin power estimates forstage m as described above for FIGS. 4 and 10A.

To achieve accelerated tracking and good estimation performance, ashorter time constant may be selected for the “fast” filter 464 used toderive Ŝ_(l,1) and Ŝ_(l,m), and a longer time constant may be selectedfor the “slow” filter 424 used to derive {circumflex over (Λ)} _(l,1).In an embodiment, the time constant for the slow filter may beapproximately 64 symbols in duration, which corresponds to 6.7 ms for128-chip symbols at a chip rate of 1.2288 Mcps in cdma2000. In anembodiment, the time constant for the fast filter may be 0 to 4 symbolsin duration, which corresponds to 0 to 416 microseconds (μs) for128-chip symbols at the chip rate of 1.2288 Mcps. A time constant of 0corresponds to no filtering, in which case the output of summer 462 isprovided as Ŝ_(l,1) or Ŝ_(l,m). Other values may also be used for thetime constants for the fast and slow filters.

FIG. 13 shows an embodiment of a process 1300 for performinginterference cancellation in multiple stages. A total power estimate andper-bin power estimates for multiple orthogonal bins are derived for afirst stage, e.g., based on the received symbols for this stage (block1312). The total power estimate for the first stage may be derived basedon a first filter having a first time constant, which may be zero orlarger. The per-bin power estimates for the first stage may be derivedbased on a second filter having a second time constant that is longerthan the first time constant. Interference cancellation is performed forthe first stage based on the per-bin power estimates for this stage(block 1314). A total power estimate is derived for a second stage,e.g., based on the received symbols for this stage (block 1316). Per-binpower estimates are also derived for the second stage based on the totalpower estimates for the first and second stages and the per-bin powerestimates for the first stage (block 1318). Interference cancellation isperformed for the second stage based on the per-bin power estimates forthis stage (block 1320). The processing for each subsequent stage may beperformed in similar manner as for the second stage.

A wireless device may maintain one or more sets of sectors such as (1)an active set containing sectors with which the wireless device is incommunication, (2) a neighbor set containing sectors that are neighborsof the sectors in the active set, (3) a candidate set containing sectorsthat are strongly received by the wireless device and are candidates forinclusion in the active set, and/or (4) some other sector sets. Theinterference cancellation may be performed in various manners. In anembodiment, interference cancellation is performed for sectors that arein the active set. The wireless device typically receives these sectorsstrongly and further has timing and multipath information to effectivelyperform interference cancellation for these sectors. In anotherembodiment, interference cancellation is performed for as many sectorsas possible based on the processing capability of the wireless device.The sectors may be selected for interference cancellation based on theirreceived signal strength or some other criteria

The interference cancellation techniques described herein may beimplemented by various means. For example, these techniques may beimplemented in hardware, firmware, software, or a combination thereof.For a hardware implementation, the processing units used to performinterference cancellation may be implemented within one or moreapplication specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, micro-controllers, microprocessors,electronic devices, other electronic units designed to perform thefunctions described herein, or a combination thereof.

For a software or firmware implementation, the interference cancellationtechniques may be implemented with modules (e.g., procedures, functions,and so on) that perform the functions described herein. The softwareand/or firmware codes may be stored in a memory (e.g., memory 292 inFIG. 2) and executed by a processor (e.g., processor 290). The memorymay be implemented within the processor or external to the processor.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. An apparatus comprising: at least one processorconfigured to derive power estimates for multiple orthogonal bins byestimating at least two components for each of the multiple orthogonalbins, one of the at least two components based on at least one gainestimate and another one based on an interference estimate of each ofthe multiple orthogonal bins and then combining the at least twocomponents, and to support interference cancellation using the powerestimates for the multiple orthogonal bins; and a memory coupled to theat least one processor.
 2. The apparatus of claim 1, wherein the atleast one processor is configured to derive a channel gain estimateforming at least part of the at least one gain estimate for acommunication channel, to derive a noise and the interference estimate,and to derive the power estimates for the multiple orthogonal bins basedon the channel gain estimate and the noise and the interferenceestimate.
 3. The apparatus of claim 2, wherein the at least oneprocessor is configured to derive an initial power estimate for a nullorthogonal bin, and to derive the noise and the interference estimatebased on the initial power estimate for the null orthogonal bin.
 4. Theapparatus of claim 2, wherein the at least one processor is configuredto obtain initial power estimates for the multiple orthogonal bins, andto derive the noise and the interference estimate based on a smallestinitial power estimate among the initial power estimates for themultiple orthogonal bins.
 5. The apparatus of claim 2, wherein the atleast one processor is configured to derive the channel gain estimatebased on a pilot received via the communication channel.
 6. Theapparatus of claim 1, wherein the at least one processor is configuredto derive a channel gain estimate forming at least part of the at leastone gain estimate for a communication channel, to derive a noise and theinterference estimate, to derive a bin gain estimate forming at leastpart of the at least one gain estimate for each of the multipleorthogonal bins, and to derive a power estimate for each orthogonal binbased on the channel gain estimate, the noise and the interferenceestimate, and the bin gain estimate for the orthogonal bin.
 7. Theapparatus of claim 6, wherein the at least one processor is configuredto derive an initial power estimate for each orthogonal bin, and toderive the bin gain estimate for each orthogonal bin based on theinitial power estimate for the orthogonal bin and a pilot powerestimate.
 8. A method comprising: deriving power estimates for multipleorthogonal bins by estimating at least two components for each of themultiple orthogonal bins, one of the at least two components based on atleast one gain estimate and another one based on an interferenceestimate of each of the multiple orthogonal bins and then combining theat least two components; and performing interference cancellation in aninterference canceller using the power estimates for the multipleorthogonal bins.
 9. The method of claim 8, wherein the deriving thepower estimates for the multiple orthogonal bins comprises deriving achannel gain estimate forming at least part of the at least one gainestimate for a communication channel, deriving a noise and theinterference estimate, and deriving the power estimates for the multipleorthogonal bins based on the channel gain estimate and the noise and theinterference estimate.
 10. The method of claim 8, wherein the derivingthe power estimates for the multiple orthogonal bins comprises derivinga channel gain estimate forming at least part of the at least one gainestimate for a communication channel, deriving a noise and theinterference estimate, deriving a bin gain estimate forming at leastpart of the at least one gain estimate for each of the multipleorthogonal bins, and deriving a power estimate for each orthogonal binbased on the channel gain estimate, the noise and the interferenceestimate, and the bin gain estimate for the orthogonal bin.
 11. Themethod of claim 8, wherein the deriving the power estimates for themultiple orthogonal bins comprises deriving a channel gain estimateforming at least part of the at least one gain estimate for acommunication channel, deriving initial power estimates for the multipleorthogonal bins, deriving a noise and the interference estimate based onthe initial power estimates, deriving bin gain estimates forming atleast part of the at least one gain estimate for the multiple orthogonalbins based on the initial power estimates, and deriving the powerestimates for the multiple orthogonal bins based on the channel gainestimate, the noise and the interference estimate, and the bin gainestimates.
 12. An apparatus comprising: means for deriving powerestimates for multiple orthogonal bins by estimating at least twocomponents for each of the multiple orthogonal bins, one of the at leasttwo components based on at least one gain estimate and another one basedon an interference estimate of each of the multiple orthogonal bins andthen combining the at least two components; and means for performinginterference cancellation using the power estimates for the multipleorthogonal bins.
 13. The apparatus of claim 12, wherein the means forderiving the power estimates for the multiple orthogonal bins is furtherconfigured for deriving a channel gain estimate forming at least part ofthe at least one gain estimate for a communication channel, for derivinga noise and the interference estimate, and for deriving the powerestimates for the multiple orthogonal bins based on the channel gainestimate and the noise and the interference estimate.
 14. The apparatusof claim 12, wherein the means for deriving the power estimates for themultiple orthogonal bins is further configured for deriving a channelgain estimate forming at least part of the at least one gain estimatefor a communication channel, for deriving a noise and the interferenceestimate, for deriving a bin gain estimate forming at least part of theat least one gain estimate for each of the multiple orthogonal bins, andfor deriving a power estimate for each orthogonal bin based on thechannel gain estimate, the noise and the interference estimate, and thebin gain estimate for the orthogonal bin.
 15. The apparatus of claim 12,wherein the means for deriving the power estimates for the multipleorthogonal bins is further configured for deriving a channel gainestimate forming at least part of the at least one gain estimate for acommunication channel, for deriving initial power estimates for themultiple orthogonal bins, for deriving a noise and the interferenceestimate based on the initial power estimates, for deriving bin gainestimates forming at least part of the at least one gain estimate forthe multiple orthogonal bins based on the initial power estimates, andfor deriving the power estimates for the multiple orthogonal bins basedon the channel gain estimate, the noise and the interference estimate,and the bin gain estimates.
 16. An apparatus comprising: at least oneprocessor configured to perform interference cancellation in a firststage, to derive a total power estimate and a per-bin power estimate forthe first stage, and to support interference cancellation in a secondstage using the total power estimate and the per-bin power estimate forthe first stage; and a memory coupled to the at least one processor. 17.The apparatus of claim 16, wherein the at least one processor isconfigured to derive per-bin power estimates for multiple orthogonalbins for the first stage, and to perform interference cancellation inthe first stage based on the per-bin power estimates.
 18. The apparatusof claim 17, wherein the at least one processor is configured to derivea total power estimate for the second stage, to derive per-bin powerestimates for the second stage based on the total power estimates forthe first and second stages and the per-bin power estimates for thefirst stage, and to perform interference cancellation in the secondstage based on the per-bin power estimates for the second stage.
 19. Theapparatus of claim 17, wherein the at least one processor is configuredto obtain power values for the multiple orthogonal bins in the firststage, to sum the power values for the multiple orthogonal bins toobtain a total power value, to derive the total power estimate for thefirst stage based on the total power value, and to derive the per-binpower estimates for the first stage based on the power values for themultiple orthogonal bins.
 20. The apparatus of claim 16, wherein for thefirst stage of interference cancellation the at least one processor isconfigured to process received samples to isolate a signal from atransmitter and obtain input samples, to transform the input samplesbased on a first transform to obtain received symbols for multipleorthogonal bins, to derive multiple gains based on the total powerestimate and the per-bin power estimate, to scale the received symbolswith the multiple gains to obtain scaled symbols, and to transform thescaled symbols based on a second transform to obtain output sampleshaving interference from the transmitter canceled.
 21. The apparatus ofclaim 20, wherein for the first stage of interference cancellation theat least one processor is configured to derive per-bin power estimatesfor the multiple orthogonal bins based on the received symbols, and toderive the multiple gains based on the per-bin power estimates.
 22. Theapparatus of claim 20, wherein for the first stage of interferencecancellation the at least one processor is configured to derive aper-bin power estimate for each orthogonal bin based on received symbolsfor the orthogonal bin, to derive a gain for each orthogonal bin basedon an inverse of the per-bin power estimate for the orthogonal bin, andto scale the received symbols for each orthogonal bin based on the gainfor the orthogonal bin.
 23. The apparatus of claim 20, wherein the atleast one processor is configured to perform resampling based on timingof the transmitter prior to transforming the input samples, and toperform extrapolation based on the timing of the transmitter aftertransforming the scaled symbols.
 24. A method comprising: performinginterference cancellation in a first stage; deriving a total powerestimate and a per-bin power estimate for the first stage; andperforming interference cancellation in a second stage using the totalpower estimate and the per-bin power estimate for the first stage. 25.The method of claim 24, wherein the performing interference cancellationin the first stage comprises deriving per-bin power estimates formultiple orthogonal bins for the first stage, and performinginterference cancellation in the first stage based on the per-bin powerestimates.
 26. The method of claim 24, wherein the deriving the per-binpower estimates for the first stage comprises deriving per-bin powerestimates for multiple orthogonal bins for the first stage.
 27. Themethod of claim 26, wherein the performing interference cancellation inthe second stage comprises deriving a total power estimate for thesecond stage, deriving per-bin power estimates for the second stagebased on the total power estimates for the first and second stages andthe per-bin power estimates for the first stage, and performinginterference cancellation in the second stage based on the per-bin powerestimates for the second stage.
 28. An apparatus comprising: means forperforming interference cancellation in a first stage; means forderiving a total power estimate and a per-bin power estimate for thefirst stage; and means for performing interference cancellation in asecond stage using the total power estimate and the per-bin powerestimate for the first stage.
 29. The apparatus of claim 28, wherein themeans for performing interference cancellation in the first stagecomprises means for deriving per-bin power estimates for multipleorthogonal bins for the first stage, and means for performinginterference cancellation in the first stage based on the per-bin powerestimates.
 30. The apparatus of claim 28, wherein means for the derivingthe per-bin power estimates for the first stage comprises means forderiving per-bin power estimates for multiple orthogonal bins for thefirst stage.
 31. The apparatus of claim 30, wherein the means forperforming interference cancellation in the second stage comprises meansfor deriving a total power estimate for the second stage, means forderiving per-bin power estimates for the second stage based on the totalpower estimates for the first and second stages and the per-bin powerestimates for the first stage, and means for performing interferencecancellation in the second stage based on the per-bin power estimatesfor the second stage.